随环境条件变化的输电线路输送容量概率建模研究

充分挖掘线路的输电潜能,提高现有电网的输电效率,是当前研究的一个热点。通常输电线路中静态载流量的计算是在保守的环境下获得,未考虑到实际运行环境。而动态载流量的计算是通过对运行环境的实时监测值,即结合实际环境温度、风速等因素,来确定其传输的极限容量,由此可以提高线路的输电效率。本文通过BP神经网络对某地区的历史气象数据进行分析和预测,由于该方法对气象预测效果较好,故将预测获得的数据作为概率模型的源数据,并提出一种基于电流密度函数的概率建模的动态增容研究方法。通过动态增容方法在某地区的应用分析,表明在迎峰度夏时可适当提高输电线路载流量,且可确保输电线路的供电可靠性。

Probabilistic modeling of transmission capacity of transmission lines with changing environmental conditions

How to calculate the transmission capacity of the transmission lines, fully extend the transmission poten-tial of lines and improve the transmission efficiency of the existing power grid is a focus of the current research. U-sually current-carrying capacity of transmission lines is obtained in conservative weather conditions. The actual op-erating environment is not taken into consideration. The calculation of dynamic-carrying capacity is made through the real-time monitoring of operating environment, including the actual environment temperature, wind speed and other factors. Then the ultimate capacity of the transmission is determined, which greatly improves the power trans-mission efficiency of lines. In this paper, the historical meteorological data of a certain area are fitted and forecast by BP neural network in the experimental environment of Matlab. Because weather at the network is better forecast, the data obtained from the forecast is the source of the probability model. And a research method of dynamic capac-ity increase based on the probability model of the current density function is proposed. The application of dynamic capacity increase in this area can be analyzed:At the peak or in the summer, transmission lines can be appropri-ately increased to 800A, and the reliability of power supply for transmission lines can be ensured.